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NBER WORKING PAPER SERIES
GLOBAL REAL ESTATE MARKETS -CYCLES AND FUNDAMENTALS
Bradford CaseWilliam N. GoetzmannK. Geert Rouwenhorst
Working Paper 7566http://www.nber.org/papers/w7566
NATIONAL BUREAU OF ECONOMIC RESEARCH1050 Massachusetts Avenue
Cambridge, MA 02138February 2000
We thanklbbotson Associates and members of the International Commercial Property Associates consortiumfor use of their data. We thank the International Center for Finance at the Yale School of Management forresearch support. The views expressed herein are those of the authors and are not necessarily those of theNational Bureau of Economic Research.
2000 by Bradford Case, William N. Goetzmann, and K. Geert Rouwenhorsi. All rights reserved. Shortsections of text, not to exceed two paragraphs, may be quoted without explicit permission provided that fullcredit, including notice, is given to the source.
Global Real Estate Markets — Cycles and FundamentalsBradford Case, William Goetzmann, and K. Geert RouwenhorstNBER Working Paper No. 7566February 2000
ABSTRACT
The correlations among international real estate markets are surprisingly high, given the
degree to which they are segmented. While industrial, office and retail properties exist all around the
world, they are not economic substitutes because of locational specificity. In addition, the broad
securitization of real estate property companies has, until recently, lagged that of other types of
companies. Never-the-less, international property returns move together in dramatic fashion. In this
paper, we use eleven years of global property returns to explore the factors influencing this co-
movement. We attribute a substantial amount of the correlation across world property markets to
the effects of changes in GNP, suggesting that real estate is a bet on fundamental economic variables
which are correlated across countries. A decomposition shows that a local production factor is more
important in some countries than in others.
Bradford Case William N. GoetzmannYale University Yale School of ManagementNew Haven, CT 06520-8200 Box 208200
New Haven, CT 06520-8200and NBER
william.goetzmann@yale.edu
K. Geert RouwenhorstYale School of ManagementNew Haven, CT 06520-8200
I. Introduction
The real estate business is distinguished from almost all others by the fact that its "product"
is not portable. For the most part, property owners compete locally for business. While inter-urban
competition for industrial, office or retail space exists, customer choice depends upon a number of
economic factors beyond the price and quality of the space. Thus, one would expect the correlation
of changes in property values across markets to diminish with the distance between them. There
are no short-term arbitrage forces preventing prices in one local market from suddenly getting hot
while prices in another local market are dropping--buildings from one market cannot be moved to
the other. For the same reasons, one might also expect international property markets to exhibit
low correlations due to the difficulties of re-locating businesses across national boundaries. Studies
measuring the diversification benefits of real estate and other asset classes suggest real estate
compares favorably in this dimension (e.g. Eicholtz. 1996. Eicholtz and Hartzell, 1996. Eicholtz et.
Al, 1998, Liu and Mci, 1998, Liu, Hartzell and Hoeseli, 1997). After looking at recent published
empirical evidence, it is clear that international real estate investment is useful for portfolio
diversification.
This logic makes the evidence about co-movement in intemational property returns all the
more striking. Goetzmann and Wachter (1996) [OW] document that the real estate crash in the early
1990's was feitby nearly every country in the world. Despite their separation by political boundaries
and great distances, the world's office markets plunged into a slump together. While economists
looked for local reasons for local decreases in property values, the reality is that there were no safe
havens for property investors in the years 1991 and 1992. Diversification did not help. The
conjecture in GW was that this slump was due to exposures to global GDP. Unfortunately,
2
insufficient time-series data prevented any formal test of the conjecture. Work by Quan and Titman
(1998), using the same data sources as GW and longer time series document that real estate is
significantly correlated to stock returns and to changes in GDP. In his in-depth analysis of the
international real estate slump of the 1990's, Renaud (1997) considers the degree to which unique
events in the late 1980's may have led to the correlated change in real estate prices and the global
economy. He also discusses the co-cyclycality of global economies and real estate. Together, these
recent studies suggest that a mix of global and local economic factors influence the world's real
estate markets.
In this paper, we use 11 years of commercial property data to examine the relationship
between GNP changes property returns. We explore the relationship in considerably more depth
than OW and take a different approach to GDP effects than Quan and Titman. Our goal is to separate
global from local economic effects on the covariance of real estate returns. In particular, we test to
see whether the correlations across global real estate markets are due to common exposures to
changes in world GDP. In addition, we estimate the incremental value of local economic
fluctuations in explaining real estate performance. We find strong evidence to show that removing
the effects of both country-specific GDP and global GDP from returns significantly decreases global
real estate market correlations. Of the two, global GDP has the greatest effect.
The implications of our results are twofold. First, world real estate markets are largely
correlated through common GDP effects. Thus, we find that even markets that are segmented by
definition can exhibit significant correlations if they are exposed to a common source of risk.
Second, we show that an investment in a global real estate portfolio is essentially a bet on broad
trends in global production.
3
II. Data
International property return data is difficult to obtain. Some authors have collected returns
from publically traded property companies in a number of countries for successful analysis. This
is useful but not always representative of the markets in all countries, and depends upon the
existence of public markets in property companies. Our data source is a recently dissolved global
consortium of real estate firms that collectively shared yield and effective rent data sampled and
assembled on an annual basis. Until recently, these firms were affiliated through International
Commercial Property Associates (ICPA), with a successor agreement with ONCOR International.
Over the past decade, their estimates of yields and effective rents were formed by firms operating
in each market according to commonly agreed upon standards. These estimates were published as
ICPA's "International Property Bulletin" and ONCOR's "World Real Estate Review," and "European
Property Bulletin." Both ICPA and ONCOR have ceased publishing these data, but London-based
Hillier Parker has continued to organize European firms to share data for European markets. In
addition, the data for Asian real estate markets is also collected by several affiliates of Hillier Parker
and published by its Hong Kong affiliate. Brooke Hillier Parker, in "Asian Property Market Survey."
Throughout the time ICPA existed, new markets entered --particularly emerging markets in Asia.
The existence of these markets in the database is undoubtably conditioned upon investor interest,
and thus potentially biased by positive performance. Thus, some of the markets included in our
database may have been "backfilled" and the paucity of data about other markets, particularly the
lack of industrial and retail information about Japan, for example, may result from recent lack of
interest in international investing there.
Since we do not have income and capital appreciation returns reported as such in the
4
database, we estimate them using yields and cap-rates. Specifically, total returns (income and
appreciation) for prime industrial, office, and retail real estate in 22 cities around the world is the
sum of the estimated yield and the change in capitalized estimated effective rents:
= + _______ - I where T1, equals estimated total return for city i at time I; i,r
equals the estimated yields (going-in cap rates); and R1 equals estimated effective rents. This
implicitly assum.es that the perpetuity formula is a reasonable approximation to value. Rents and
yields were estimated for consistently-defined standard properties in the prime commercial districts
of each city by commercial real estate firms in each country.
While these sources present data for a large number of cities, the analysis must be restricted
to those markets for which estimated rents and yields are available for every year during the period
1987-1997. Table 1 shows the 22 markets from 21 countries included in the analysis under this
criterion. Two German markets, Dusseldorf and Frankfurt, are inc]uded. because the required data
were available for both cities.
While the effective rent data are given in nominal terms and generally denominated in local
currency, from the perspective of a U.S. investor it is more relevant to consider returns expressed
in U.S. dollars. Therefore the nominal foreign-currency returns were converted to real U.S. dollar
terms by using the exchange rate in effect at the end of each year and then deflating by the U.S.
inflation rate.
Table I shows the geometric means, arithmetic means, and standard deviations of the total
5
return series computed for each type of real estate, and for the stock market total return series. The
average total returns are in some cases spectacu]arly high: for example, the geometric means for the
11-year period exceed 20 percent for industrial space and office space in Hong Kong, Portugal,
Singapore, and Thailand and for retail space in Portugal and Thailand. At the same time, these
investments were extremely volatile, with standard deviations exceeding 40 percent in several cases.
The volatility for the U.S. is only slightly higher than obtained from other appraisal-based indices.
As a check on whether the yield-based return series' for the U.S. correspond to appraisal-
based returns in the U.S. we measured the correlation to the NCREIIF index . The U.S. Industrial
property index had a correlation of .84 to the NCREIF index and the U.S. Office property index had
a correlation of .56. Neither had a significant correlation to the NARE1T index of equity real estate
investment trusts. Thus, we are using series' that more closely resemble and indeed track the
appraisal-based indices commonly used in the U.S to measure commercial real estate performance.
Figure 1 shows the trend in real estate returns over the period 1987 through 1997 for each
country and property type. The crash of the early 1991 through 1993 shows up clearly as a majority
of markets and property types experiences negative real returns during the period. There were almost
no safe havens during this period. Only Hong Kong, Singapore, Malaysia and Portugal had strong
positive returns in the three-year slump. The year 1997 closely resembles the early 90's, when most
real returns were negative. The comovement between markets also shows up in the correlations.
We do not report the entire 60 by 60 correlation matrix, however, on average, the correlations within
property types across countries ranged between 0.33 and 0.44.
Figure 2 shows the dollar-denominated changes in GDP for each country, deflated by the
U.S. CPI over the same time period. The eleven-year period contains two booms and two busts in
6
the global economy. The relationship to the real estate cycles is unmistakable --property returns
clearly fluctuate with GDP changes. In the next section we more formally test the effects of GDP on
real estate market trends and correlations.
ifi. Methodology
In this section we test the hypothesis that international real estate markets are correlated
through GDP. To do this, we first remove the effects of a country's own GDP on its property return
series through univariate linear regression of the return series on contemporaneous GDP changes.
Then for each property type, we compare the correlation matrices of the raw returns and of the
regression residuals. Finally, we perform a paired t-test of the off-diagonal elements in the return
and residual correlation matrixes to determine whether the difference in the means is significant.
Rejecting the null hypothesis of equality of returns and residual correlations would provide strong
support for the hypothesis that the co-movement of global real estate markets is driven by common
exposure to factors affecting production. We then perform the same test after removing the effects
of an equal-weighted index of GDP changes. This second test allows us to examine whether
exposure the global economy explains correlations. In the second test, we expect the average
correlation to decrease, since we are simply extracting a common factor. This is not necessarily the
true when we extract each country's own GDP effect, however. If local GDP effects are important
and uncorrelated across countries, then removing them could increase correlations of the residuals.
If the local the GDP factor reflected both important local economic effects and globa' economic
effects, then correlations could either increase or decrease.
7
III.] T-test results
Table II reports the results of the paired t-test for each property-type in the sample. The
correlation of dollar-denominated returns is relatively high for each property-type, although highest
for office properties. It is tempting to attribute this high correlation to the fact that customers for
class A office space in major countries around the world have increasingly become the same 100
multi-national firms, however we no empirical evidence to support this conjecture. The table shows
that purging the returns of the effects of own-country GDP changes results in a significant drop in
average correlation across country. The largest proportional drop was in the Industrial property
sector -- the sector most closely tied with production -- which dropped to a third of it value. Notice
that purging the returns of the equal-weighted GDP factor results in an even larger and strongly
significant decrease in average correlation across countries. In fact, the average correlation in the
Industrial property sector drops to 0.087.
Another way to evaluate the effects of removing local GDP and global GDP is to examine
the change in summed variance. Removing the global factor decreases variance by 58 to 72 percent.
Removing only the local factor results in 20 to 30 percent variance reduction.
1112 Global vs. Local Effects
The analysis thus far shows that the effect on covariance of removing an equal-weighted
global GDP factor is, on average, important. Cross-sectional differences may be relevant, however.
For example, the recent performance of Asian real estate markets suggests that local GDP factors
may overwhelm global trends. Th order to compare the relative effects of global vs. local GDP
effect, we use econometric methods to separate the two. In Table ifi we report the R2 from
8
regressions in which we separate the GDP factor into local and common components. To do this,
we first regress each GDP series on the equal-weighted global GDP factor and save the residuals.
Next, we use these residuals in a regression, together with the equal-weighted global GDP factor as
variables to explain each real estate market. I.e. in stage 1, we estimate the local GDP factor X1
with the regression: G1, = a+ J67, + ), where G1 is the change in GDP for country i at time
and G is the equal-weighted global GDP factor realization at time t. Tn stage 2, we use X1 and G
as regressors to explain the total return series for a real estate market: 77, =a+flG + 44, + ç and
save the R2 from this regression as RA2. Finally, we use only G as an explanatory
variable: 77, = a+ + and save the R2 from this regression as RB2.
In order to determine the importance of the local GDP component to the global GDP
component, we take the difference in R2 and divide by the R2 from the regression on the global factor
alone. That is: (RA2 -R2)/R2. It is important to note that the number of time-series observations
in each regression is only eleven. Thus, we would expect relatively high R2 from a regression with
two explanatory variables. We have not used adjusted-R2, although this would be appropriate if we
were explicitly testing an hypothesis with this ratio. With these caveats in mind, we use the ratio
on]y for the purposes of indicating the tendency in each market for the local factor to predominate
over the global factor. In fact for most markets, the g]obal factor is most important-- the incremental
variance explained by the local residual variable is less that the amount of variance explained by the
global factor. There are a few markets that differ from this norm, however. We find that Australia,
Canada, Hong Kong, Thailand, U.K. and to some extent, the U.S., Malaysia and Spain are countries
where local GDP effects dominate global influences. We might expect this for the U.S. and the U.K.
9
since the GDP factor is equal-weighted and these two countries' GDP's would obviously have a
larger than equal weight were we using gross GDP weights, or market capitalization weights. This
is not true for some of the other countries, however. The table suggests that, while fundamental
economic factors explain much of the performance of local real estate markets, the effects of local
economic deviations from global trends are more important for some countries, the U.S. included.
111.3 Time-series regressions
Although we have only eleven years of data, it is useful to further consider how the global
GDP factor is r&ated to the fluctuations of property returns. In Table ifi weregress equal-weighted
portfolios of property-types on the equal-weighted GDP factor, and include the one-year lagged
values of GDP changes and the lagged property-type return itself, in order to control for
autocorrelation in the regression error. Note that in each case, the contemporaneous GDP change
is significantly related to returns, and the lagged value is not. In this regard, our time-series results
are broadly consistent with Quan and Titman (1998). This is potentially important, because one
criticism of all appraisal-related real estate data is that itcaptures "asking rents" not "effective rents."
Asking rents are typically sticky and thus area stale measure of real estate markets. The lack of a
lagged relationship between GDP and real estate returns suggests that contemporaneous economic
conditions are reflected in our data. This does not mean our return series' are unpredictable random
walks. lii two of the three regressions, office and retail, the lagged value of the return series is also
significant, indicating strong persistence.
10
1114 US. Time-Series Regression
Although long-term international data is unavailable, we have time-series data for the U.S.
commercial property market that extends from 1960. We use Ibbotson Associates Business Real
Estate total annual return series from 1960 through 1994, and the NCREIF index for years 1995
through 1997. This is the dependent variable in a regression that includes four years of lagged values
and contemporaneous and four lagged values of U.S. GDP growth. The results of this regression are
reported in Table 4. Contemporaneous GDP growth has a coefficient of .65 and is strongly
significant. The inclusion of four lags for each series appears to eliminate the autocorrelation of the
errors in the regression -- the Durbin-Watson statistic is 2. This regression indicates that in at least
one market where we do have long-term data, the relation between GDP growth and real estate
returns is a strong one.
IV. Diversification
The co-movement of real estate markets through exposure to global GDP changes is
potentially meaningful to investors because it suggests that despite the obvious importance of local
economic conditions to the determinants of property values, diversification has its limits. One way
to explore these limits is to consider how the volatility of a real estate portfolio decreases as more
markets are added to the portfolio. Figure 3 shows the average percentage reduction in volatility
achieved by adding additional countries in sequence, by property type. Country stock markets are
provided for a comparison. The greatest percentage reduction in risk through international
diversification is achieved by the industrial property type and the least percentage of reduction in risk
through international diversification is achieved by office markets. Both office markets and retail
11
markets appear to offer slightly lower relative benefits to international diversification than do equity
markets. In general, however the figure suggests that the international diversification benefits to real
estate are similar in magnitude to those of the equity markets. This is somewhat surprising in light
of the fundamentally location-specific nature of real estate as an investment.
Figure 4 shows the result of removing the global GDP factor from each series. In effect, the
figure shows the results of a portfolio continuously hedged against GDP risk. Notice that the risk
of the industrial portfolio drops considerably, and is well below the equity portfolio limit. The
lower bound is at 13.7 % of the variance of a portfolio with a single country industrial real estate
portfolio investment.
V Conclusions
Our analysis of the re]ationship between changes in GDP and international property returns
suggests that the cross-border correlations of real estate are due in part to common exposure to
fluctuations in the global economy, as measured by an equal-weighted index of international GDP
changes. Country-specific GDP changes help explain more of the variation in real estate returns.
Indeed, in some countries local factors explain considerably more, in percentage terms, than do
global factors. Our study suggests that, while real estate is fundamentally local, demand for space
apparently responds to contemporaneous changes in the global economy. Our analysis of
international diversification suggests that portfolio volatility is reduced by cross-border property
investment, but that only one asset class, Industrial properties, actually yields greater diversification
benefits than international equity market diversification.
12
References
Barry, Christopher-B.. Mauricio Roderiguez and Joseph B.Lipscomb, 1996, "DiversificationPotential from Real Estate Companies in Emerging Capital Markets" Journal of Real EstatePortfolio Management 2(2). 1996, pages 107-18.
Eicholtz, Piet, 1996, "Does International Diversification Work Better for Real Estate than forStocks and Bonds?" Financial Analysts Journal, January-February, 57-62.
Eicholtz, Piet. Ronald Huisman, Kees Koeddijk, Lisa Sehuin, 1998, "Continental Factors inInternational Real Estate Returns" Real Estate Economics 26:3 493-509.
Eicholtz, Piet, 1996, "The Stability of the Covari.ances of International Property Share Returns"Journal of Real Estate Research,11(2), 1996, pages 149-58.
Eichholtz, Piet, Hartzell, David J., 1996"Property Shares, Appraisals and the Stock Market: AnInternational Perspective," Journal of Real Estate Finance and Economics;12(2), March 1996,pages 163-78.
Goetzmann, William and Susan Wachter, 1996, "The Global Real Estate Crash: Evidence FromAn International Database," Yale School of Management Working Paper.
Liu, Crocker and Jian-Ping Mci, 1998, "The Predictability of International Real Estate Markets,Echange Rate Risks and Diversification Consequences," Real Estate Economics 26:1, pp.3-39.
Liu, Crocker, Hartzell, David J.. Hoesli, Martin E., 1997, "Internatitmal Evidence on RealEstate Securities as an Inflation Hedge" Real-Estate-Economics;25(2), Summer 1997, pages193-22 1.
Quan, Daniel C and Sheridan Titman, 1998, "Do Real Estate Prices and Stock Prices Movetogether? An International Analysis," forthcoming, Real Estate Economics.
Renaud, Bertrand, 1994, 'The 1985-1994 Global Real Estate Cycle: Are There LastingBehavioral and Regulatory Lessons?" Journal of Real Estate Literature 5(1), January 1997,pages 13-44.
13
Tab
le I:
Sum
mar
y S
tatis
tics
of R
eal U
.S.
Dol
lar-
Dcn
omin
ated
Ret
urns
S
umm
ary
stat
istic
s fo
r th
e IC
PA
dat
a by
cou
ntry
and
pro
pert
y ty
pe o
ver
the
perio
d 19
87 th
roug
h 19
97. R
etur
ns a
re e
stim
ated
fro
myi
cids
an
d ef
fect
ive
rent
s as
dis
cuss
ed in
the
text
, and
con
vert
ed to
U.S
. dol
lars
and
def
late
d by
the
U.S
. inf
iatio
n ra
te.
Indu
stria
l P
rope
rtie
s O
ffice
Pro
pert
ies
Ret
ail P
rope
rtie
s
Cou
ntry
or c
ity
Geo
met
ric A
rithm
etic
S
tand
ard
Ser
ial
Geo
met
ric
Arit
hmet
ic
Sta
ndar
d S
eria
l G
eom
etric
A
rithm
etic
S
tand
ard
Ser
ial
Ret
urn
Ret
urn
Dev
iatio
n C
orre
latio
n R
etur
n R
etur
n D
evia
tion
Cor
rela
tion
Ret
urn
Ret
urn
Dev
iatio
n C
orre
latio
n
Aus
tral
ia
12.1
0 14
.46
2473
00
7 4.
39
7.56
28
.63
0.39
1.
67
5.77
31
.18
0.57
B
elgi
um
7.51
9.
93
23.8
2 -0
.20
8.34
9.
61
17.1
1 0.
33
11.5
5 14
.13
25.0
5 -0
.50
Can
ada
NA
N
A
NA
N
! •2
.96
4.32
16
.51
0.07
N
A
NA
N
A
NA
D
enm
ark
5.18
6.
73
20.6
0 -0
.16
4.50
5.
14
12.0
7 -0
.25
3.69
4,
76
16.7
8 0.
39
Fin
land
9.
08
13.3
0 30
.53
0.52
4.
54
9.47
32
.98
0.47
10
.24
14.9
5 33
.19
0.25
F
ranc
e 5.
29
7.03
20
.76
-0.0
9 3.
47
5.57
22
.19
0.40
8.
88
11.0
1 22
.70
0.16
G
-Dus
seld
orf
6.58
8.
46
21.2
6 0.
05
7.25
8.
50
17.0
6 0.
18
9.56
11
.42
23.1
0 -0
.20
G-F
rank
furt
2.
96
5,13
20
.40
0.43
7.
61
9.43
21
.29
0.76
7.
37
10.3
9 27
.48
0.41
H
ongK
ong
20.8
6 23
.14
25.2
6 0.
32
16.4
5 22
,79
41.3
4 0.
26
12.6
5 13
.53
14.3
3 0.
05
Irel
and
18.0
1 20
.20
24.1
4 0.
05
11.7
8 13
.25
19.2
9 0.
04
11.5
6 12
.56
15.6
1 0.
70
Italy
11
.18
13.1
6 21
.85
0.24
1.
00
5.16
32
.53
0.46
1.
57
7.06
34
.20
-0.5
4 Ja
pan
NA
N
A
NA
N
? -1
7.35
-1
2.21
31
.33
0.25
N
A
NA
N
A
NA
M
alay
sia
-0.3
3 5,
41
39.9
4 -0
.55
4.19
9.
53
34.2
5 -0
.28
NA
N
A
NA
N
A
Net
herla
nds
10.2
5 11
.61
19.1
9 -0
.04
10.0
8 11
.46
18.7
5 0.
20
7.77
9.
07
17.4
8 -0
.54
Por
tuga
l 28
.99
34.8
8 45
.52
0.43
17
.80
21.0
9 30
.04
0.67
27
.22
31.8
9 42
.18
0.13
S
inga
pore
26
.90
31.0
6 33
.62
-0.5
1 15
.73
18.6
6 27
.68
0.13
12
.61
15.7
5 30
.00
0.66
S
pain
14
.46
21.1
1 44
.60
0.76
3.
81
10.2
8 40
.23
0.51
4.
05
lt.54
46
.12
0.59
S
wed
en
5.88
9.
71
27.2
7 0.
24
2.71
8.
81
35.8
5 0.
22
9.31
15
.10
36.2
7 0.
29
Sw
itzer
land
-5
.18
-3.2
5 22
.72
-0.0
7 -[
0.42
-2
.20
47.4
3 0.
46
-8.5
4 -7
.35
15.8
4 -0
.42
Tha
iland
9.
75
16,8
0 40
.50
-0.1
7 5.
29
12.6
8 45
.94
0.26
27
.20
35.3
1 51
.70
0.36
U
K
13.1
4 15
.82
26.9
2 0.
35
4.12
7.
94
29.8
8 0.
30
4.24
7.
17
24.9
9 0.
18
US
A
7.18
8.
68
18.9
7 0.
49
4.68
4.
95
8.05
-0
.16
NA
N
A
NA
N
A
14
Tab
le 11
: 'I'
est o
f the
Equ
ality
of M
eans
Thi
s ta
ble
repo
rts t
he re
sults
of t
estin
g th
e hy
poth
esis
tha
t the
ave
rage
off-
diag
onal
ele
men
t in t
he c
orre
latio
n m
atrix
of p
rope
rty-
type
ret
urns
acr
oss
coun
trie
s in
the
sam
ple
is e
qual
to
the
aver
age
off-
diag
onal
ele
men
t in
the
cor
rela
tion
mat
rix o
f res
idua
ls t
hat r
esul
t fro
m th
e re
turn
ser
ies
bein
g re
gres
sed
on it
s ow
n ch
ange
in
GD
P. A
ll re
turn
s an
d re
sidu
als
and
GD
P c
hang
es a
re re
al U
.S.
dolla
r den
omin
ated
. P
leas
e no
te,
[-st
atis
tics
have
not
bee
n co
rrec
ted
for
doub
led
off-
diag
onal
val
ues
in t
he c
orre
latio
n m
atric
es.
Indu
stria
l O
ffice
R
etai
l
Ave
rage
Cor
rela
tion o
f Ret
urns
0.
334
0.43
9 0.
363
Ave
rage
Cor
rela
tion
of O
wn
GD
P R
esid
uals
0.
129
0.26
5 0.
162
(-st
at o
f Pai
red
1-te
st o
f dif
fere
nce
11.1
57
11.3
35
10.6
16
Ave
rage
Cor
rela
tion
of W
orld
GD
P R
esid
uals
0.
087
0.27
0 0.
121
(-st
at o
f Pai
red
1-te
st o
f dif
fere
nce
13.4
52
10.8
07
15.7
87
Var
ianc
e R
educ
tion;
Ow
n G
DP
0.
284
0.30
9 0.
199
Var
ianc
e R
educ
tion;
EW
GD
P F
acto
r 0.
7 19
0.
602
0.58
4
15
Tab
le I
II: G
loba
l vs.
Loc
al G
DP
Eff
ects
R2
from
(1)
regr
essi
ons
of re
turn
s on
the
equa
lwei
ghte
d g'
obal
CD
P fa
ctor
and
the o
rtho
gona
l loc
al G
DP
fact
or fo
r eac
h m
arke
t and
(2)
regr
essi
ons
of re
turn
s on
thee
qual
-wei
ghte
d glo
bal G
DPf
acto
ralo
ne. T
heor
thog
onal
loca
l CD
Pfac
tori
s con
stru
cted
by r
egre
ssin
geac
h co
untr
y's
chan
ge in
OD
P on
the
equa
l-w
eigh
ted g
loba
l GD
P fa
ctor
, whi
ch is
con
stru
cted
as
an e
qual
-wei
ghte
d po
rtfo
lio o
f eac
h co
untr
y's
chan
ge in
GD
P.
The
rat
io i
s th
e di
ffer
ence
in R
2 be
twee
n th
e tw
o re
gres
sion
s, sc
aled
by
the
R2
of th
e re
gres
sion
on
the
glob
al G
DP
fact
or a
lone
. R
atio
s fo
r co
untr
ies
whe
re t
he
incr
emen
tal
R2
due
to l
ocal
GD
P ch
ange
s exc
eeds
R2
due
to t
he g
loba
l G
DP
fact
or a
re in
hol
d. 1
(2 a
re u
nadj
uste
d.
Indu
stri
al P
rope
rtie
s O
ffic
e Pr
oper
ties
Ret
ail
Prop
ertie
s C
ount
ry o
r city
R
atio
R
atio
R
atio
lo
cal
+ g
loba
l gl
obal
aR
2/gl
obal
R2
loca
l +
glo
bal
glob
al
AR
2/gl
obal
R2
loca
l +
glo
bal
glob
al
AR
2/gl
obal
R2
Aus
tral
ia
0.60
4 0.
069
7.71
9 0.
962
0.00
9 2.
446
0.87
3 0.
289
2.01
6 B
elgi
um
0.78
7 0.
748
0.05
3 0.
737
0.69
1 0.
067
0.61
1 0.
588
0.03
8 C
anad
a N
A
NA
N
A
0.62
8 0.
012
52.3
95
NA
N
A
NA
D
enm
ark
0.46
4 0.
397
0.16
9 0.
799
0.73
5 0.
086
0.44
4 0.
318
0.39
7 Fi
nlan
d 0.
451
0.38
6 0.
170
0.84
1 0.
352
1.38
7 0.
543
0.44
6 0.
217
Fran
ce
0.4.
45
0.44
3 0.
005
0.50
7 0.
482
0.05
1 0.
374
0.30
2 0.
238
G-D
usse
ldor
f 0.
478
0.42
5 0.
125
0.31
4 0.
300
0.05
0 0.
449
0.16
6 1.
700
0-Fr
ankf
urt
0.52
3 0.
516
0.01
4 0.
482
0.42
4 0.
136
0.39
0 0.
389
0.00
3 Fl
ong
Kon
g 0.
409
0.00
3 15
8.16
9 0.
451
0.05
7 6.
847
0.17
0 0.
001
244.
065
Irel
and
0.85
9 0.
855
0.00
4 0.
359
0.30
8 0.
167
0.58
4 0.
533
0.09
6 Jt
aly
0.45
7 0.
255
0.78
9 0.
425
0.27
5 0.
545
0.28
7 0.
258
0.11
4 Ja
pan
NA
N
A
NA
0.
462
0.44
7 0.
032
NA
N
A
NA
M
alay
sia
0.31
0 0.
310
0.00
0 0.
251
0.11
1 1.
254
NA
N
A
NA
N
ethe
rlan
ds
0.83
3 0.
830
0.00
3 .
0.68
4 0.
678
0.00
9 0.
514
0.51
2 0.
005
Port
ugal
0.
106
0.10
2 0.
040
0.35
4 0.
334
0.05
9 0.
464
0.43
8 0.
059
Sing
apor
e 0.
308
0.33
1 0.
147
0.77
6 0.
759
0.02
3 0.
123
0.10
8 0.
134
Spai
n 0.
450
0.16
7 1.
689
0.51
6 0.
358
0.44
0 0.
196
0.18
1 0.
082
Swed
en
0.61
9 0.
357
0.73
5 0.
580
0.43
7 0.
326
0.61
9 0.
572
0.08
1 Sw
itzer
land
0.
328
0.32
4 0.
013
0.23
8 0.
188
0.26
7 0.
499
0.48
4 0.
031
Tha
iland
0.
517
0.16
9 2.
051
0.21
6 0.
072
1.98
7 0.
769
0.03
0 24
,557
U
K
0.54
1 0.
249
1.16
9 0.
589
0.16
8 2.
501
0.28
5 0.
218
0.38
0 U
SA
0.32
6 0.
009
34.2
63
0.17
0 0.
102
0.67
3 N
A
NA
N
A
16
Tab
le I
V:
Tim
e-Se
ries
Reg
ress
ion
of P
rope
rty-
Typ
e Po
rtfo
lios
on A
GU
P Fa
ctor
Res
ults
from
reg
ress
ions
of e
qual
-wei
ghte
d pro
pert
y-ty
pe p
ortf
olio
s on
an
equa
l-w
eigh
ted G
DP
fact
or
over
the
peri
od 1
987
thro
ugh
1997
. Indu
stri
al
Off
ice
Ret
ail
coef
[-
stat
co
ef
I-st
at
coef
t-
stat
Inte
rcep
t 0.
010
0.22
7 0.
005
0.12
3 -0
.013
-0
.329
Equ
al-W
eigh
ted G
DP
1.
321
3.56
0 0.
808
1.87
6 1.
160
3.22
4
Equ
al-W
eigh
ted
GD
[-1]
-0
.369
-0
.588
-.
86!
-1.3
44
-0.3
43
-0.6
47
Pro
pert
y Po
rtfo
lio [-
I]
0.48
0 0.
350
0.70
9 2.
325
0.58
7 2.
307
Dur
bin-
Wat
son
1.86
1.
78
1.48
R-S
quar
e 0.
76
754
.778
17
Tab
le V
: U
.S. B
usin
ess
Rea
l Est
ate
tota
l Ret
urn
and
Cha
nges
in U
.S. G
DP
U.S
. Bus
ines
s Rea
l Est
ate
is ta
ken
fror
nlbb
otso
n A
ssoc
iate
s E
nCor
rda(
abas
e and
mea
sure
s th
e to
tal r
etur
n to
a p
ortfo
lio of
com
mer
cial
real
est
ate
over
the
perio
d 19
60 th
roug
h 19
94.
For
199
5 th
roug
h 19
97, t
he N
CR
EIF
tot
al re
turn
inde
x is
use
d.
Per
iod:
196
0- 1
997
Coe
ffic
ient
T
-Sta
tistic
Inte
rcep
t -0
.044
-1
.450
GN
P
0.53
3 2.
178
GN
P t-
1 -0
.399
-1
.436
GN
P t-
2 0.
779
2.74
2
GN
P t-
3 -0
.2 17
-0
.691
GN
P t-
4 0.
407
1.35
0
Bus
ines
s Rea
l E
stat
e t-
1 1.
068
5.20
8
Bus
ines
s R
eal
Est
ate
t-2
-0.7
16
-2.4
83
Bus
ines
s R
eal
Est
ate
t-3
0.33
1 1.
164
Bus
ines
s R
eal
Est
ate
t-4
-0.2
24
-1.1
61
Dur
bin-
Wat
sop
2.03
R-s
quar
ed
0.78
5
18
I .20
1.00
0.80
0.60
0.40
0.20
0.00
-0.2
0
-0.4
0
-0.6
2
Tim
e • Au
stra
lia!
Bel
gium
I • De
nmar
k I
• Finl
and
I F
ranc
e I
• 6-D
usse
ldor
f I • G-
Fra
nktu
rt I
l-Ion
gKon
g I
• Irela
nd I
Italy
M
alay
sia
I N
ethe
rland
s I
Por
tuga
l I
F
Sin
gapo
re I
Till
Spa
in I
Sw
eden
I T
i Sw
itzer
land
T
haila
nd I
UnK
ingd
om I
US
A I
Aus
tral
ia 0
Bel
gium
0
Can
ada
0 D
enm
ark
0 • Fi
nlan
d 0
Fra
nce 0
G-D
usse
ldor
t 0
G-F
rank
furt
0
Hon
gKon
g 0
Irel
and
0 • Ita
ly 0
Ja
pan
0 • M
alay
sia 0
Net
herla
nds 0
P
ortu
gal 0
S
inga
pore
0
Spa
in 0
Sw
eden
0
iiTi S
witz
erla
nd 0
T
haila
nd 0
T
i UnK
ingd
om 0
U
SA
0
Aus
tral
ia R
B
elgi
um R
D
enm
ark
R
Fin
land
R
• Fra
nce
R
G-D
usse
ldor
l H
• G-F
rank
lurt
H
. I-
iong
Kon
g R
Irel
and
H
Italy
A
Net
herla
nds A
P
ortu
gal H
fl S
inga
pore
R
Spa
in R
• Sw
eden
H
• Switz
erla
nd H
• Th
aila
nd H
U
nkin
gdon
i A
Fig
ure
1 A
nnua
l R
etur
ns F
or a
ll M
arke
ts a
nd P
rope
rty T
ypes
: 19
87 -
199
7
19
Ret
urn
Val
ues
1.48
I .40
Dec
D
ec
1987
1988
Dec
1989
1Q90
1901
1992
1993
1994
Dec
Dec
1995
1996
Dec
1997
Ret
urn
Val
ues
0.34
—
0.28
—
0.24 —
-0,04—-'
-0.08—
-0.12—
-0.16——
-0.2
0
-0,2
4—
-0.2
8—
-0.3
2—
I II
Tim
e
biLi
1t
• Fran
ce G
• G
erm
any
G • Ho
ngK
ong
C
Irel
and
C
Ti S
inga
pore
I
Spa
in G
T
i Sw
eden
C
Sw
itzer
land
C
0.20
—
0.16
—
0.12
—
0.08
—
0.04
—
0.00
—
Li II
II .1
1
-0.3
6 I Dec
I Dec
D
ec
I Dec
D
ec
Dec
D
ec
Dec
I Dec
F
Dec
1967
1988
1989
1990
1991
1992
1993
• Aust
ralia
C
Fl B
elgi
um C
• C
anad
a C
• D
enm
ark
G
' F
inla
nd C
• Ital
y C
0 T
haila
nd C
Ja
pan
C
UnK
ingd
om C
M
alay
sia
C
US
A C
• Ne
ther
land
s C
T
i Por
tuga
l C
Figu
re 2:
Dol
lar-
Den
omin
ated
chan
ges
in G
DP
defl
ated
by
the
u.g9
cpi,
1987
-. 19
97
Dec
1997
Ben
efits
of G
loba
l D
iver
sific
atio
n in
Rea
l Est
ate
by P
rope
rty-
Typ
e 0
-___
____
____
____
____
__
0-
0 I __
Indu
stria
l I
Offi
ce
I R
etai
l
L S
tock
Mar
kets
0 (0 -
10
2!
20
Fig
ure
3
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